Design and Definition of a New Decision Support System Aimed to the Hierarchization of Patients Candidate to Be Admitted to Intensive Care Units

Integrating tabular data through image conversion for enhanced diagnosis: A novel intelligent decision support system for stratifying obstructive sleep apnoea patients using convolutional neural networks

Objective

High-dimensional databases make it difficult to apply traditional learning algorithms to biomedical applications. Recent developments in computer technology have introduced deep learning (DL) as a potential solution to these difficulties. This study presents a novel intelligent decision support system based on a novel interpretation of data formalisation from tabular data in DL techniques. Once defined, it is used to diagnose the severity of obstructive sleep apnoea, distinguishing between moderate to severe and mild/no cases.

Methods

The study uses a complete database extract from electronic health records of 2472 patients, including anthropometric data, habits, medications, comorbidities, and patient-reported symptoms. The novelty of this methodology lies in the initial processing of the patients' data, which is formalised into images. These images are then used as input to train a convolutional neural network (CNN), which acts as the inference engine of the system.

Results

The initial tests of the system were performed on a set of 247 samples from the Pulmonary Department of the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain), with an AUC value of ≈ 0.8.

Conclusions

This study demonstrates the benefits of an intelligent decision support system based on a novel data formalisation approach that allows the use of advanced DL techniques starting from tabular data. In this way, the ability of CNNs to recognise complex patterns using visual elements such as gradients and contrasts can be exploited. This approach effectively addresses the challenges of analysing large amounts of tabular data and reduces common problems such as bias and variance, resulting in improved diagnostic accuracy.

Design of an Intelligent Decision Support System Applied to the Diagnosis of Obstructive Sleep Apnea

Obstructive sleep apnea (OSA), characterized by recurrent episodes of partial or total obstruction of the upper airway during sleep, is currently one of the respiratory pathologies with the highest incidence worldwide. This situation has led to an increase in the demand for medical appointments and specific diagnostic studies, resulting in long waiting lists, with all the health consequences that this entails for the affected patients. In this context, this paper proposes the design and development of a novel intelligent decision support system applied to the diagnosis of OSA, aiming to identify patients suspected of suffering from the pathology. For this purpose, two sets of heterogeneous information are considered. The first one includes objective data related to the patient's health profile, with information usually available in electronic health records (anthropometric information, habits, diagnosed conditions and prescribed treatments). The second type includes subjective data related to the specific OSA symptomatology reported by the patient in a specific interview. For the processing of this information, a machine-learning classification algorithm and a set of fuzzy expert systems arranged in cascade are used, obtaining, as a result, two indicators related to the risk of suffering from the disease. Subsequently, by interpreting both risk indicators, it will be possible to determine the severity of the patients' condition and to generate alerts. For the initial tests, a software artifact was built using a dataset with 4400 patients from the Álvaro Cunqueiro Hospital (Vigo, Galicia, Spain). The preliminary results obtained are promising and demonstrate the potential usefulness of this type of tool in the diagnosis of OSA.